Low-Complexity PAPR Reduction Technique for OFDM Systems Using Biased Subcarriers
Bibliographic record
Abstract
A peak-to-average power ratio (PAPR) reduction technique using biased subcarriers is proposed and investigated. A known time-domain reference sample (Dref) is used to bias the subcarriers at the transmitter, and the same bias is used at the receiver to recover the sequence of original subcarrier samples. A closed-form analytical expression for complementary cumulative distribution function for PAPR has been derived and is illustrated as a function of the introduced bias. The effectiveness of the proposed technique is evaluated both analytically and numerically. Analytical and simulation results confirm that significant reduction in PAPR can be achieved. For example, it is shown that nearly 9.45-dB reduction in 0.1% PAPR can be achieved for a 16-QAM orthogonal frequency division multiplexing system with 1024 subcarriers. Numerical results show that the average bit error rate performance of the proposed system does not degrade relative to the original system. It is found that the proposed technique has the lowest complexity among the various available techniques for PAPR reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".